{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic回归技术实现糖尿病发病预测   -数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入必要工具模块\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据\n",
    "train = pd.read_csv('pima-indians-diabetes.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据区域\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#查看基本信息\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从特征统计量的数值看，该数据集部分数据为0属于缺失数据，比如：体重和血压数值为0则无实际意义，需要进行处理。\n",
    "具体来说，下列变量的最小值为0时数据无意义： 1、血浆葡萄糖浓度 2、舒张压 3、肱三头肌皮褶厚度 4、餐后血清胰岛素 5、体重指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "#显示为0的NaN列统计数\n",
    "print((train[NaN_col_names]==0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从统计结果看，第1，2，5列的0值数量相对较少，3，4列的数量较大，快接近半数，需要在特征工程中进行数据替换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看变量分布以及对应关系\n",
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看怀孕次数\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1af50458a58>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='pregnants',hue='Target',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过查看怀孕与目标结果，发现确实存在一定的关联，当怀孕数量超过7时，发病的比例变大。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#血浆葡萄糖浓度\n",
    "sns.distplot(train['Plasma_glucose_concentration'],kde=False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='Plasma_glucose_concentration',data=train,hue='Target')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#blood_pressure\n",
    "sns.distplot(train['blood_pressure'],kde=False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意到有30多个血压为0的样本，是无意义的数据，下一步特征工程中进行处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看blood_pressure与目标Target之间的关系\n",
    "sns.violinplot(x='Target',y='blood_pressure',data=train,hue='Target')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('blood_pressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看三头肌皮褶厚度（单位：mm）\n",
    "sns.distplot(train['Triceps_skin_fold_thickness'],kde=False)\n",
    "plt.xlabel('Triceps_skin_fold_thickness')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='Triceps_skin_fold_thickness',data=train,hue='Target')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Triceps_skin_fold_thickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看餐后血清胰岛素（单位:mm）\n",
    "sns.distplot(train['serum_insulin'],kde=False)\n",
    "plt.xlabel('serum_insulin')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='serum_insulin',data=train,hue='Target')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('serum_insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看 BMI 体重指数（体重（公斤）/ 身高（米）^2）\n",
    "sns.distplot(train['BMI'],kde=False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='BMI',data=train,hue='Target')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1af507d26a0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#通过bar图查看与标签的关系\n",
    "BMIDF = train.groupby(['BMI','Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看 Diabetes_pedigree_function，糖尿病家系作用\n",
    "sns.distplot(train['Diabetes_pedigree_function'],kde=False)\n",
    "plt.xlabel('Diabetes_pedigree_function')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看年龄分布\n",
    "sns.distplot(train['Age'],kde=False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1af510bdb00>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#年龄与标签之间的关系\n",
    "ADF = train.groupby(['Age','Target'])['Age'].count().unstack('Target').fillna(0)\n",
    "ADF[[0,1]].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1af512bde48>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看特征之间的相关性\n",
    "data_corr = train.corr().abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data=data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出特征与特征之间的相关性不是很强。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征工程部分：\n",
    "经过分析数据后，具体来说，下列变量的最小值为0时数据无意义： 1、血浆葡萄糖浓度 2、舒张压 3、肱三头肌皮褶厚度 4、餐后血清胰岛素 5、体重指数\n",
    "在Pandas的DataFrame中，通过replace()函数可以很方便的将我们感兴趣的数据子集的值标记为NaN。\n",
    "标记完缺失值之后，可以利用isnull()函数将数据集中所有的NaN值标记为True，然后就可以得到每一列中缺失值的数量了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0,np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从0值替换的结果来看，NaN_col_names里面统计到的对应列0值总数，下面我们用中值对0值项进行填充。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medv = train.median()\n",
    "train = train.fillna(medv)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\preprocessing\\data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "#数据标准化处理\n",
    "y_train = train['Target']\n",
    "X_train = train.drop(['Target'],axis=1)\n",
    "#保存特征工程的结果\n",
    "feat_names = X_train.columns\n",
    "#数据标准化模块\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#进行预处理\n",
    "X_train = MinMaxScaler().fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将特征处理后的数据保存到文件\n",
    "X_train = pd.DataFrame(columns=feat_names,data=X_train)\n",
    "train = pd.concat([X_train,y_train],axis=1)\n",
    "train.to_csv('FE_pima-indians-diabetes.csv',index = False,header = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
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       "      <th>serum_insulin</th>\n",
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       "      <th>Diabetes_pedigree_function</th>\n",
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       "      <th>Target</th>\n",
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       "      <td>0.428571</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.202454</td>\n",
       "      <td>0.038002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.185096</td>\n",
       "      <td>0.509202</td>\n",
       "      <td>0.943638</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.352941                      0.670968        0.489796   \n",
       "1   0.058824                      0.264516        0.428571   \n",
       "2   0.470588                      0.896774        0.408163   \n",
       "3   0.058824                      0.290323        0.428571   \n",
       "4   0.000000                      0.600000        0.163265   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.304348       0.133413  0.314928   \n",
       "1                     0.239130       0.133413  0.171779   \n",
       "2                     0.239130       0.133413  0.104294   \n",
       "3                     0.173913       0.096154  0.202454   \n",
       "4                     0.304348       0.185096  0.509202   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.234415  0.483333       1  \n",
       "1                    0.116567  0.166667       0  \n",
       "2                    0.253629  0.183333       1  \n",
       "3                    0.038002  0.000000       0  \n",
       "4                    0.943638  0.200000       1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null float64\n",
      "Plasma_glucose_concentration    768 non-null float64\n",
      "blood_pressure                  768 non-null float64\n",
      "Triceps_skin_fold_thickness     768 non-null float64\n",
      "serum_insulin                   768 non-null float64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null float64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据基本情况\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#准备训练数据\n",
    "y_train = train['Target']\n",
    "X_train = train.drop(['Target'],axis=1)\n",
    "#保存特征名字以备后用（可视化）\n",
    "feat_names = X_train.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.49200504 0.52099888 0.47770562 0.45251079 0.4886484 ]\n",
      "cv logloss is: 0.4863737457555152\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#开始Logistic回归\n",
    "#导入模型\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "lr = LogisticRegression()\n",
    "loss = cross_val_score(lr,X_train,y_train,cv=5,scoring='neg_log_loss')\n",
    "print('logloss of each fold is: ',-loss)\n",
    "print('cv logloss is:',-loss.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#正则化的 LogisticRegression 及参数调优\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4755664138749241\n",
      "{'C': 10, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "#打印最佳的模型参数\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图给出了L1正则和L2正则下、不同正则参数C对应的模型在训练集上测试集上的logloss。可以看出在训练集上C越大（正则越少）的模型性能越好；但在测试集上当C=10时性能最好（L2正则）"
   ]
  }
 ],
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